Design of a Novel Skin Lesion Predictor Model Using Hybrid Particle Swarm Optimization and Convolutional Neural Networks

Abstract

The prediction of skin cancer at the earlier stage is extremely essential for melanoma. There is a need for intellectual computer analysis for skin lesions. The segmentation of lesion boundaries is vital to accurately identify the lesions from the dermoscopic images where the diagnosis is complex for various skin lesion types. Thus, some pre-processing steps are required to attain higher sensitive lesion boundary segmentation and classification. Initially, pre-processing is done with median filter to offer reputation of boundary preservation and does not in-cooperate newer pixel values to the processed image. Next is the process of image segmentation for Region of Interest (ROI) and non-Region of Interest ([Formula: see text] using the Jaccard Distance segmentation process. The novelty of the work relies on the inclusion of compression techniques to make the access easier without any loss. The extracted regions are encoded with Freeman Chain Coding and compressed with Lempel–Ziv–Welch (LZW) and Zero Tree Wavelet (EZW) for ROI and non-Region of Interest regions. Finally, image classification is done with Hybrid Particle Swarm Optimization and Convolutional Neural Networks ([Formula: see text]PSO-CNN). The simulation is done with TensorFlow & Python environment and the proposed model outperforms the existing standard approaches. Some metrics like objective function, confusion matrix, accuracy, precision, [Formula: see text]-measure, and recall are evaluated. The model attains 77.5% accuracy, 86.36% precision, 77.5% recall and 77.92% [Formula: see text]-measure for proposed [Formula: see text]PSO-CNN which is higher than the standard CNN model.